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Mathematical Modeling Tools to Study Preharvest Food Safety

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  • Authors: Cristina Lanzas1, Shi Chen2
  • Editors: Kalmia Kniel3, Siddhartha Thakur4
  • VIEW AFFILIATIONS HIDE AFFILIATIONS
    Affiliations: 1: Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, NC 27607; 2: Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, NC 27607; 3: Department of Animal and Food Science, University of Delaware, Newark, DE; 4: North Carolina State University, College of Veterinary Medicine, Raleigh, NC
    • Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.PFS-0001-2013
  • Received 02 February 2015 Accepted 22 April 2015 Published 19 August 2016
  • Cristina Lanzas, [email protected]
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  • Abstract:

    This article provides an overview of the emerging field of mathematical modeling in preharvest food safety. We describe the steps involved in developing mathematical models, different types of models, and their multiple applications. The introduction to modeling is followed by several sections that introduce the most common modeling approaches used in preharvest systems. We finish the chapter by outlining potential future directions for the field.

  • Citation: Lanzas C, Chen S. 2016. Mathematical Modeling Tools to Study Preharvest Food Safety. Microbiol Spectrum 4(4):PFS-0001-2013. doi:10.1128/microbiolspec.PFS-0001-2013.

Key Concept Ranking

Foodborne Pathogens
0.6854147
Salmonella enterica
0.6039341
Infectious Diseases
0.5936253
0.6854147

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2016-08-19
2019-09-20

Abstract:

This article provides an overview of the emerging field of mathematical modeling in preharvest food safety. We describe the steps involved in developing mathematical models, different types of models, and their multiple applications. The introduction to modeling is followed by several sections that introduce the most common modeling approaches used in preharvest systems. We finish the chapter by outlining potential future directions for the field.

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Mathematical Modeling Tools to Study Preharvest Food Safety
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Citation: Lanzas C, Chen S. 2016. Mathematical modeling tools to study preharvest food safety. microbiolspec 4(4): doi:10.1128/microbiolspec.PFS-0001-2013
/content/microbiolspec/10.1128/microbiolspec.PFS-0001-2013.fig1
FIGURE 1

The modeling cycle.

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microbiolspec/4/4/PFS-0001-2013-fig1.gif
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FIGURE 1

The modeling cycle.

Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.PFS-0001-2013
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/content/microbiolspec/10.1128/microbiolspec.PFS-0001-2013.fig2
FIGURE 2

Time series (days) of infection prevalence simulated by a stochastic susceptible-infectious model. Ten realizations of the model are presented.

microbiolspec/4/4/PFS-0001-2013-fig2_thmb.gif
microbiolspec/4/4/PFS-0001-2013-fig2.gif
Image of FIGURE 2

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FIGURE 2

Time series (days) of infection prevalence simulated by a stochastic susceptible-infectious model. Ten realizations of the model are presented.

Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.PFS-0001-2013
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/content/microbiolspec/10.1128/microbiolspec.PFS-0001-2013.fig3
FIGURE 3

Examples of flow charts for epidemiological models commonly used for modeling foodborne pathogens in the animal host. –poultry ( 25 ). –dairy cattle ( 14 ). –swine ( 72 ).

microbiolspec/4/4/PFS-0001-2013-fig3_thmb.gif
microbiolspec/4/4/PFS-0001-2013-fig3.gif
Image of FIGURE 3

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FIGURE 3

Examples of flow charts for epidemiological models commonly used for modeling foodborne pathogens in the animal host. –poultry ( 25 ). –dairy cattle ( 14 ). –swine ( 72 ).

Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.PFS-0001-2013
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/content/microbiolspec/10.1128/microbiolspec.PFS-0001-2013.fig4
FIGURE 4

An example of a deterministic compartmental infectious disease model. The flowchart represents an SIRS model. Compartments S, I, and R track the number of individuals who are susceptible, infectious, and recovered, respectively. The triangles represent change in the number of individuals in the compartments. Models that contain both demographic (e.g., births and deaths) and infection flows are called endemic models. Compartmental models are often described mathematically as ordinary differential equations. The ordinary differential equations here describe the inflows and outflows by which the number of individuals in each epidemiological state (S, I, and R) changes over time. The total population, N is equal to S + I + R; υ = birth and death rate; γ = recovery rate of infectious individuals; β = transmission coefficient (transmission is frequency dependent); and = immunity loss rate. The inflow and outflow of a compartment is reflected in the equation. For instance, the S compartment has four arrows associated with it: two inflows and two outflows. The two inflows are birth and loss of immunity from recovered compartment (hence the + sign in the first equation); the two outflows are natural death and infection to infectious compartment (hence the – sign in the first equation). The basic reproduction number of this model.

microbiolspec/4/4/PFS-0001-2013-fig4_thmb.gif
microbiolspec/4/4/PFS-0001-2013-fig4.gif
Image of FIGURE 4

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FIGURE 4

An example of a deterministic compartmental infectious disease model. The flowchart represents an SIRS model. Compartments S, I, and R track the number of individuals who are susceptible, infectious, and recovered, respectively. The triangles represent change in the number of individuals in the compartments. Models that contain both demographic (e.g., births and deaths) and infection flows are called endemic models. Compartmental models are often described mathematically as ordinary differential equations. The ordinary differential equations here describe the inflows and outflows by which the number of individuals in each epidemiological state (S, I, and R) changes over time. The total population, N is equal to S + I + R; υ = birth and death rate; γ = recovery rate of infectious individuals; β = transmission coefficient (transmission is frequency dependent); and = immunity loss rate. The inflow and outflow of a compartment is reflected in the equation. For instance, the S compartment has four arrows associated with it: two inflows and two outflows. The two inflows are birth and loss of immunity from recovered compartment (hence the + sign in the first equation); the two outflows are natural death and infection to infectious compartment (hence the – sign in the first equation). The basic reproduction number of this model.

Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.PFS-0001-2013
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FIGURE 5

Network models are usually represented as graphs. A graph contains a set of nodes (individuals) and edges (contacts) that are associated with these nodes. Shown is an example with five interacting calves. The calves with contact with each other have a line (edge) between them to indicate their mutual relationship . The graph can be translated to an adjacency matrix, which contains as many rows and columns as there are nodes . The elements of the matrix record information about the edges between each pair of nodes, with 1 indicating a contact and 0 meaning no contact.

microbiolspec/4/4/PFS-0001-2013-fig5_thmb.gif
microbiolspec/4/4/PFS-0001-2013-fig5.gif
Image of FIGURE 5

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FIGURE 5

Network models are usually represented as graphs. A graph contains a set of nodes (individuals) and edges (contacts) that are associated with these nodes. Shown is an example with five interacting calves. The calves with contact with each other have a line (edge) between them to indicate their mutual relationship . The graph can be translated to an adjacency matrix, which contains as many rows and columns as there are nodes . The elements of the matrix record information about the edges between each pair of nodes, with 1 indicating a contact and 0 meaning no contact.

Source: microbiolspec August 2016 vol. 4 no. 4 doi:10.1128/microbiolspec.PFS-0001-2013
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